{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 数据准备及探索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 数据导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import recall_score  \n",
    "from sklearn.metrics import accuracy_score \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "sns.reset_orig() \n",
    "\n",
    "df = pd.read_csv('diabetes.csv', header=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 数据基本信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 目标变量分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MwnUhJGkOmmzJ0d+dpF9V1Qd6qEeSNCYmO4P4Xkvbc4G1wE8BBoQkzWKTLTl64cR2kucD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\n82mNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17218fc8a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Target 分布，看各类样本分布是否均衡\n",
    "fig = plt.figure()\n",
    "sns.countplot(df.Outcome, palette=['#b3dacb','#83b2c1'])\n",
    "plt.xlabel('Outcome')\n",
    "plt.ylabel('Number of occurrences')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "各类样本不均衡，正样本数目较少。为避免对少数类分类准确率降低，可在分类器中设置类别权重，提高正样本分类错误的代价；同时在交叉验证中应采用StratifiedKFold（缺省），在每折采样时根据各类样本按比例采样。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 输入变量分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1721939afd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 怀孕次数分布\n",
    "fig = plt.figure()\n",
    "sns.countplot(df.Pregnancies, palette=sns.color_palette(\"GnBu\", 18, desat=0.8))\n",
    "plt.xlabel('Pregnancies', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\H\\Install\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "image/png": 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3MDQ8zN/+/gkefvMNG68wCya8QlDVQRG5HXge8AIPqmq5iNzi7L8P2AFcA1QB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\nCcEYYwwA/w8pEY3MMxzTdwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x172195097f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 血浆葡萄糖浓度分布\n",
    "fig = plt.figure()\n",
    "sns.distplot(df.Glucose.values, bins=30, kde=True, color='#589693')\n",
    "plt.xlabel('Glucose Density', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\H\\Install\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "image/png": 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xbmvMglBVfvnmMfwZGZTn2WWvbrhp/Qaae3u41NXldhQzR7EUiXIg8tbJRmdZ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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1721a5f87f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 舒张压分布\n",
    "fig = plt.figure()\n",
    "sns.distplot(df.BloodPressure.values, bins=30, kde=True, color='#589693')\n",
    "plt.xlabel('Blood Pressure', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\H\\Install\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "image/png": 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+M6uImnbems3sK8Hf9RYzuyk5VcePgj9OorqnmA2UA3eaWXlyq2pTE/Aldy8H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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1721a708b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 三头肌皮肤褶层厚度分布\n",
    "fig = plt.figure()\n",
    "sns.distplot(df.SkinThickness.values, bins=30, kde=True, color='#589693')\n",
    "plt.xlabel('Skin Thickness', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\H\\Install\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1721a84e908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2小时血清胰岛素含量分布\n",
    "fig = plt.figure()\n",
    "sns.distplot(df.Insulin.values, bins=80, kde=True, color='#589693')\n",
    "plt.xlabel('Insulin Density', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\H\\Install\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "image/png": 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0N5svEAwE6B4e0sLqPhWwANvr6nm75SLdw0NexxGP+ObkrqS+q3191JSUaJlF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\naUaFX0Qkzajwi4ikGRV+EZE0878BIrJ1+i6J5BgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1721a83cbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 体重指数分布\n",
    "fig = plt.figure()\n",
    "sns.distplot(df.BMI.values, bins=30, kde=True, color='#589693')\n",
    "plt.xlabel('BMI', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "以上五项特征，均在零值附近出现异常分布：\n",
    "\n",
    "Glucose、BloodPressure、SkinThickness、BMI 四项特征在零值处存在部分离群点，根据总体分布和特征含义（血糖浓度、血压、皮肤褶层厚度、BMI不能为0）判断，应为缺失值；\n",
    "\n",
    "Insulin 特征在零值附近大量集中，虽然胰岛素浓度可能为零，但根据样本分布趋势和日常经验，真正的零值应该极少，大部分零值实际应为缺失值，故统一作为缺失值处理。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\H\\Install\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "image/png": 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JITcnh9ycCLmRCMV5eeTplpJZYcZwN7MI8DXg3cApYLeZPejuhyY0ew+wLni8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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1721a6546d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 糖尿病家族史变量分布\n",
    "fig = plt.figure()\n",
    "sns.distplot(df.DiabetesPedigreeFunction.values, bins=30, kde=True, color='#589693')\n",
    "plt.xlabel('Diabetes Pedigree Function', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\H\\Install\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1721951e390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 年龄分布\n",
    "fig = plt.figure()\n",
    "sns.distplot(df.Age.values, bins=30, kde=True, color='#589693')\n",
    "plt.xlabel('Age', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GvG2MWeyOf/CCl/oUUjIyKFWyZM50bMmSpGRk5Bs/eV4ijWrX9kZq8i8kZ2QQ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\nYxEgSZIkLTIWAZIkSdIi8z/SYN7UJhWBdgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17219446a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 通过非缺失值查看各特征间相关关系\n",
    "df_nan = df[['Glucose','BloodPressure','SkinThickness','Insulin','BMI']].applymap(lambda e: np.nan if e == 0 else e)\n",
    "df = pd.concat([df.iloc[:,0], df_nan, df.iloc[:, 6:]], axis = 1)\n",
    "df_check = df.dropna()   \n",
    "data_corr = df_check.corr().abs()\n",
    "plt.figure(figsize=(12, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可利用相关性较强的特征对缺失数据进行填补。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 缺失值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 删除缺失值大于等于 4 个的样本\n",
    "df=df.dropna(thresh=6, axis=0) \n",
    "#df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 删除 Glucose 缺失的样本（数量较少且不好填充）\n",
    "df = df[df['Glucose'].notnull()]\n",
    "#df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于 BloodPressure，没有与其相关系数较大的特征，并且可以观察到其分布大致符合正态分布，可采用均值进行填补。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 对 BloodPressure 的缺失值进行均值填补\n",
    "index = df[df.BloodPressure.isnull()].index\n",
    "df['BloodPressure'] = df['BloodPressure'].fillna(df.BloodPressure.mean())\n",
    "#df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SkinThickness 和 Insulin缺失值较多，可将缺失值置为零，同时赋予一个标示该值是否缺失的新特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 为SkinThickness和Insulin的缺失值设置新特征\n",
    "df['SkinThickness_nan'] = 0\n",
    "df.ix[df['SkinThickness'].isnull(), ['SkinThickness_nan']] = 1\n",
    "df['SkinThickness'] = df['SkinThickness'].fillna(0)\n",
    "df['Insulin_nan'] = 0\n",
    "df.ix[df['Insulin'].isnull(), ['Insulin_nan']] = 1\n",
    "df['Insulin'] = df['Insulin'].fillna(0)\n",
    "#df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BMI 与 SkinThickness 有一定的相关性，可利用 SkinThickness 进行分组，取每组中 BMI 的中位数进行填补。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 利用 SkinThickness 分组计算 BMI 的均值填补缺失值\n",
    "df['SkinThickness_cut'] = pd.cut(df['SkinThickness'], 20, labels=range(20), include_lowest=True)\n",
    "df['BMI'] = df['BMI'].fillna(df.groupby('SkinThickness_cut')['BMI'].transform('median'))\n",
    "df = df.drop('SkinThickness_cut', 1)\n",
    "#df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 划分训练数据和测试数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 划分训练数据集和测试数据集\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = df.drop('Outcome', 1).values\n",
    "y = df['Outcome'].values\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 数据标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)\n",
    "#mmc = MinMaxScaler()\n",
    "#X_train = mmc.fit_transform(X_train)\n",
    "#X_test = mmc.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Logistic 回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'C': 0.10000000000000001, 'penalty': 'l1'}\n",
      "CV Accuracy: 0.755\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# 模型训练与超参选择\n",
    "lr = LogisticRegression(class_weight='balanced')\n",
    "param_range1 = np.logspace(-4, 3, 8)\n",
    "grid = {'penalty':['l1','l2'],\n",
    "        'C': param_range1}\n",
    "gs_lr = GridSearchCV(estimator=lr, param_grid=grid, cv=10, scoring='accuracy')\n",
    "gs_lr.fit(X_train, y_train)\n",
    "\n",
    "print('Best parameters: %s' % gs_lr.best_params_)\n",
    "print('CV Accuracy: %.3f' % gs_lr.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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iM8siNmg9vUmbF4FzzCzDzHKAscAKYqeixplZjsXuErsw2C9t5OF3VtHg8I1z\nB0UdRUQSRGgnpt29zsxuB14ldpXTI+6+zMxuC56f4u4rzOwVYAnQADzk7ksBzOxZYiv91RFbHvbB\nsLLKobbvrWHa/DVcPqIf/bvnRB1HRBJEqCOZ7j4DmNFk35Qm2/cA9zRz7N3A3WHmk+Y9Pqecqpp6\nbpugKcxF5KCoB70lwVTV1PHoe6v47Cm9GNonN+o4IpJAVDDkEE8vqGB7Va0WSBKRT1HBkE/U1jfw\nP7PLGFPYnTMGdo86jogkGBUM+cT0xetYt7NavQsRaZYKhgDQ0ODcP6uUk/vkcv5Q3c8iIp+mgiEA\nvLFiIys37dECSSJyWCoYgrtz38xSCrp15NLT+kYdR0QSlAqGMG/VNhZX7ODW8waRka4fCRFpnj4d\nhPtnltKzcxZXFfdvubGIpCwVjBS3bN1OZn28mZvPLiI7UwskicjhqWCkuCmzyujcIYPrxw2MOoqI\nJDgVjBRWvnUvf1myjuvGDaBLx8yo44hIglPBSGEPzC4jIy2Nr59dFHUUEUkCKhgpatOuap4tqeRL\nZxTQ64TsqOOISBJQwUhRj7y7mrqGBm49TwskiUh8VDBS0K7qWp6cW84lp/WlsGenqOOISJJQwUhB\nU+eWs3t/Hd/UAkkichRUMFJMdW09j7yzmvNOymN4fpeo44hIElHBSDHPLqxky5796l2IyFFTwUgh\ndfUNPDi7jJH9uzJukBZIEpGjo4KRQv7y9/Ws2ValKcxF5JioYKQId+f+maUMzuvERaf0jjqOiCQh\nFYwUMfPjzXy4YTe3TRhMWpp6FyJy9FQwUsT9M0vp2yWbSSPzo44iIklKBSMFLCzfxvxV2/incweR\nlaH/5SJybDKiDiDhu39mGV1zMrlmjBZIEjletbW1VFZWUl1dHXWUo5KdnU1BQQGZmcc+M7UKRjv3\n8cbdvLFiI9/97BBysvS/W+R4VVZWkpubS2FhYdJcbejubN26lcrKSoqKjn12ap2faOemzCqlY2Y6\nN55VGHUUkXahurqaHj16JE2xADAzevTocdy9IhWMdqxyexXTF69j8pgBdOuUFXUckXYjmYrFAa2R\nOdSCYWYTzewjM1tpZncdps35ZrbYzJaZ2axG+7ua2bNm9qGZrTCzs8LM2h499PYqAL5xrhZIEmlP\nOnfu/Kl9s2fPZvTo0WRkZPDss8+G8r6hFQwzSwd+D1wCDAMmm9mwJm26AvcBl7v7qcBVjZ7+LfCK\nu58MjABn7msSAAAKSklEQVRWhJW1Pdq6Zz9PLVjDFaPy6de1Y9RxRCRkAwYM4NFHH+Xaa68N7T3C\nHAUdA6x09zIAM3sKmAQsb9TmWuB5d18D4O6bgrZdgPOAm4L9NUBNiFnbncfeW83+ugZum6AFkkRS\nQWFhIQBpaeGdOAqzYOQDFY22K4GxTdqcBGSa2UwgF/ituz8OFAGbgT+Y2QhgIXCHu+9t+iZmdgtw\nC8QqrMCe/XU8Nqeczw3rzYm9cqOOI9Ju/fylZSxft6tVX3NYvxO4+7JTW/U1W0vUg94ZwBnApcDF\nwE/N7KRg/2jgfncfBewFmh0DcfcH3b3Y3Yvz8vLaKHZie2r+Gnbuq+U2TWEuIq0ozB7GWqDxnWIF\nwb7GKoGtQc9hr5nNJjZe8TZQ6e7zgnbPcpiCIYfaX1fP/7xdxlmDejBqQLeo44i0a4naEwhLmD2M\nBcAQMysysyzgGmB6kzYvAueYWYaZ5RA7ZbXC3TcAFWY2NGh3IYeOfchhvLhoHRt37eeb56t3ISKt\nK7QehrvXmdntwKtAOvCIuy8zs9uC56e4+wozewVYAjQAD7n70uAlvgM8GRSbMuDmsLK2F/UNzpTZ\npZza7wTOHdIz6jgiEpKqqioKCgo+2f7+97/Pueeey5VXXsn27dt56aWXuPvuu1m2bFmrvm+oc0W4\n+wxgRpN9U5ps3wPc08yxi4HiMPO1N68t20DZ5r387tpRSXljkYjEp6Ghodn9lZWVob5v1IPe0krc\nnftnlVLYI4dLhveNOo6ItEMqGO3Ee6VbWVK5k1snDCZdCySJSAhUMNqJ+2eW0iu3A18crQWSRCQc\nKhjtwJLKHbyzcgtfP6eIDhnpUccRkXZKBaMdmDKrlNzsDK4dqzvdRSQ8KhhJrmzzHv66dANfPWsg\nudnHvpKWiEhLVDCS3IOzy8hKT+Om8ZrCXCRVNDe9+b333suwYcM4/fTTufDCCykvL2/191XBSGIb\ndlbz3PuVfKW4P3m5HaKOIyIRGjVqFCUlJSxZsoQvf/nL/PCHP2z191DBSGIPv1NGg8Mt52kKc5FU\nd8EFF5CTkwPAuHHjQrmJL9Q7vSU8O6pq+OO8NVx2el/6d8+JOo5IavrrXbDh7637mn1Og0t+fVwv\n8fDDD3PJJZe0UqCDVDCS1BNzytlbU89tmmRQRBqZOnUqJSUlzJo1q+XGR0kFIwntq6nnD++t5jMn\n9+LkPidEHUckdR1nT6C1vfHGG/zqV79i1qxZdOjQ+uOaKhhJ6JmSCrbtrdEU5iLyiUWLFnHrrbfy\nyiuv0KtXr1DeQwUjydTWN/Dg7DKKB3bjzMLuUccRkQg0N735jBkz2LNnD1dddRUQW7J6+vSmSxAd\nHxUM4LL/fofq2vqoY8Rlf10Da3fs4xeTUmulLxE5qLnpzb///e+H/r4qGMDgvE7U1Dc/v3wiuvjU\n3lwwNJwup4jI4ahgAL+5ZlTUEUREEp5u3BMRkbioYIiIHCV3jzrCUWuNzCoYIiJHITs7m61btyZV\n0XB3tm7dSnZ29nG9jsYwRESOQkFBAZWVlWzevDnqKEclOzv7kEtxj4UKhojIUcjMzKSoKDWXE9Ap\nKRERiYsKhoiIxEUFQ0RE4mLJNNLfEjPbDBzruoQ9gS2tGCdMyZQVkitvMmWF5MqbTFkhufIeT9aB\n7p4XT8N2VTCOh5mVuHtx1DnikUxZIbnyJlNWSK68yZQVkitvW2XVKSkREYmLCoaIiMRFBeOgB6MO\ncBSSKSskV95kygrJlTeZskJy5W2TrBrDEBGRuKiHISIicVHBaIaZ3WlmbmY9o85yOGb2SzNbYmaL\nzew1M+sXdabDMbN7zOzDIO8LZtY16kxHYmZXmdkyM2sws4S8SsbMJprZR2a20szuijrPkZjZI2a2\nycyWRp2lJWbW38zeMrPlwc/AHVFnOhIzyzaz+Wb2QZD352G+nwpGE2bWH/gcsCbqLC24x91Pd/eR\nwMvAv0Ud6AheB4a7++nAx8CPI87TkqXAF4HZUQdpjpmlA78HLgGGAZPNbFi0qY7oUWBi1CHiVAfc\n6e7DgHHAtxP8v+1+4DPuPgIYCUw0s3FhvZkKxqf9J/BDIKEHd9x9V6PNTiRwXnd/zd3rgs25wPFN\nmRkyd1/h7h9FneMIxgAr3b3M3WuAp4BJEWc6LHefDWyLOkc83H29u78ffL0bWAHkR5vq8DxmT7CZ\nGTxC+yxQwWjEzCYBa939g6izxMPMfmVmFcB1JHYPo7GvAX+NOkSSywcqGm1XksAfasnKzAqBUcC8\naJMcmZmlm9liYBPwuruHljflpjc3szeAPs089RPgfxE7HZUQjpTV3V90958APzGzHwO3A3e3acBG\nWsoatPkJsS7/k22ZrTnx5JXUZWadgeeA7zbpzSccd68HRgZjgy+Y2XB3D2W8KOUKhrt/trn9ZnYa\nUAR8YGYQO23yvpmNcfcNbRjxE4fL2owngRlEWDBaympmNwFfAC70BLiW+yj+2yaitUD/RtsFwT5p\nBWaWSaxYPOnuz0edJ17uvsPM3iI2XhRKwdApqYC7/93de7l7obsXEuvmj46qWLTEzIY02pwEfBhV\nlpaY2URi40KXu3tV1HnagQXAEDMrMrMs4BpgesSZ2gWL/bX4MLDC3e+NOk9LzCzvwFWHZtYRuIgQ\nPwtUMJLXr81sqZktIXYaLZEv//sdkAu8HlwGPCXqQEdiZleaWSVwFvAXM3s16kyNBRcQ3A68SmxQ\n9hl3XxZtqsMzs2nAHGComVWa2dejznQEZwM3AJ8JflYXm9nnow51BH2Bt4LPgQXExjBeDuvNdKe3\niIjERT0MERGJiwqGiIjERQVDRETiooIhIiJxUcEQEZG4qGCIHAUz29NyqyMe/6yZDQq+7mxmD5hZ\nqZktNLOZZjbWzLLMbLaZpdyNtZLYVDBE2oiZnQqku3tZsOshYpPyDXH3M4CbgZ7BhIJ/A66OJqlI\n81QwRI6BxdwT3Dz5dzO7OtifZmb3Bet/vG5mM8zsy8Fh1wEH5tUaDIwF/tXdGwDcfZW7/yVo++eg\nvUjCUJdX5Nh8kdj6AyOAnsACM5tN7E7hQmLrVPQidif2I8ExZwPTgq9PBRYHE8c1ZylwZijJRY6R\nehgix+YcYJq717v7RmAWsQ/4c4A/uXtDMA/ZW42O6QtsjufFg0JSY2a5rZxb5JipYIi0nX1AdvD1\nMmBEsHre4XQAqkNPJRInFQyRY/M2cHWweE0ecB4wH3gX+FIwltEbOL/RMSuAEwHcvRQoAX4ezJCK\nmRWa2aXB1z2ALe5e21bfkEhLVDBEjs0LwBLgA+BN4IfBKajniE2NvxyYCrwP7AyO+QuHFpBvAL2B\nlWa2lNja15uC5y4I2oskDM1WK9LKzKyzu+8JegnzgbPdfUOwXsFbwfbhBrsPvMbzwF3u/nEbRBaJ\ni66SEml9LweL2mQBvzywCJe77zOzu4mtv73mcAcHiyL9WcVCEo16GCIiEheNYYiISFxUMEREJC4q\nGCIiEhcVDBERiYsKhoiIxEUFQ0RE4vL/ATnFCSr6ZweAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1721a6d0c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv_scores_lr = gs_lr.cv_results_['mean_test_score'].reshape(len(param_range1), 2)\n",
    "fig = plt.figure()\n",
    "plt.plot(np.log10(param_range1), cv_scores_lr[:,0],label='L1')\n",
    "plt.plot(np.log10(param_range1), cv_scores_lr[:,1],label='L2')\n",
    "plt.xlabel('log(C)')\n",
    "plt.ylabel('cv-accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy: 0.767\n",
      "Test Accuracy: 0.724\n",
      "Train Recall: 0.774\n",
      "Test Recall: 0.736\n"
     ]
    }
   ],
   "source": [
    "# 利用调好的最佳超参重新训练模型\n",
    "lr = gs_lr.best_estimator_\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# 在测试集上进行预测和评估\n",
    "print('Train Accuracy: %.3f' % accuracy_score(y_train, lr.predict(X_train)))\n",
    "print('Test Accuracy: %.3f' % accuracy_score(y_test, lr.predict(X_test)))\n",
    "print('Train Recall: %.3f' % recall_score(y_train, lr.predict(X_train)))\n",
    "print('Test Recall: %.3f' % recall_score(y_test, lr.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 线性SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'C': 1.0}\n",
      "CV Accuracy: 0.750\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "# 模型训练与超参选择\n",
    "lsvm = LinearSVC(class_weight='balanced')\n",
    "param_range2 = np.logspace(-4, 3, 8)\n",
    "grid = {'C': param_range2}\n",
    "gs_lsvm = GridSearchCV(estimator=lsvm, param_grid=grid, cv=10, scoring='accuracy')\n",
    "gs_lsvm.fit(X_train, y_train)\n",
    "\n",
    "print('Best parameters: %s' % gs_lsvm.best_params_)\n",
    "print('CV Accuracy: %.3f' % gs_lsvm.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6/NPAjzvayd3/BPzpmNfuPeb5w8DDx7y2HI0oF5FuVtA/k7NGDqSsspqvlY4P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\nISIiMVFhiIhITFQYIiISk/8PyoTkVXsuKdUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1721a8549e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv_scores_lsvm = gs_lsvm.cv_results_['mean_test_score']\n",
    "fig = plt.figure()\n",
    "plt.plot(np.log10(param_range2), cv_scores_lsvm)\n",
    "plt.xlabel('log(C)')\n",
    "plt.ylabel('cv-accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy: 0.758\n",
      "Test Accuracy: 0.743\n",
      "Train Recall: 0.717\n",
      "Test Recall: 0.755\n"
     ]
    }
   ],
   "source": [
    "# 利用调好的最佳超参重新训练模型\n",
    "lsvm = gs_lsvm.best_estimator_\n",
    "lsvm.fit(X_train, y_train)\n",
    "\n",
    "# 在测试集上进行预测和评估\n",
    "print('Train Accuracy: %.3f' % accuracy_score(y_train, lsvm.predict(X_train)))\n",
    "print('Test Accuracy: %.3f' % accuracy_score(y_test, lsvm.predict(X_test)))\n",
    "print('Train Recall: %.3f' % recall_score(y_train, lsvm.predict(X_train)))\n",
    "print('Test Recall: %.3f' % recall_score(y_test, lsvm.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出，线性 SVM 在测试数据集上的表现比 Logistic 回归更好，因为其优化策略为最大化分类决策面的间隔，意味着以一定程度的确信度将两个类别分开，降低了过拟合的风险，可以有较好的泛化能力。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. RBF核的SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'C': 100.0, 'gamma': 0.01}\n",
      "CV Accuracy: 0.765\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "# 模型训练与超参选择\n",
    "svm = SVC(kernel='rbf', class_weight='balanced')\n",
    "Cs = np.logspace(-1, 5, 7)\n",
    "gammas = np.logspace(-4, -1, 4)\n",
    "grid = {'C': Cs,\n",
    "        'gamma': gammas}\n",
    "gs_svm = GridSearchCV(estimator=svm, param_grid=grid, cv=10, scoring='accuracy')\n",
    "gs_svm.fit(X_train, y_train) \n",
    "\n",
    "print('Best parameters: %s' % gs_svm.best_params_)\n",
    "print('CV Accuracy: %.3f' % gs_svm.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gB3tr9vLy8Zc5UH9gVLqoovlXug+YLSIzsE72W4DPXLqTiEwA1gGfHeyx41Zl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1721a751f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv_scores_svm = gs_svm.cv_results_['mean_test_score'].reshape(len(Cs),len(gammas))\n",
    "fig = plt.figure()\n",
    "for i, gamma in enumerate(gammas):\n",
    "    plt.plot(np.log10(Cs), cv_scores_svm[:,i],label='gamma '+ str(gammas[i]))\n",
    "plt.xlabel('log(C)')\n",
    "plt.ylabel('cv-accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy: 0.821\n",
      "Test Accuracy: 0.743\n",
      "Train Recall: 0.882\n",
      "Test Recall: 0.736\n"
     ]
    }
   ],
   "source": [
    "# 利用调好的最佳超参重新训练模型\n",
    "svm = gs_svm.best_estimator_\n",
    "svm.fit(X_train, y_train)\n",
    "\n",
    "# 在测试集上进行预测和评估\n",
    "print('Train Accuracy: %.3f' % accuracy_score(y_train, svm.predict(X_train)))\n",
    "print('Test Accuracy: %.3f' % accuracy_score(y_test, svm.predict(X_test)))\n",
    "print('Train Recall: %.3f' % recall_score(y_train, svm.predict(X_train)))\n",
    "print('Test Recall: %.3f' % recall_score(y_test, svm.predict(X_test)))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
